WSN Based Thermal Modeling: A New Indoor Energy Efficient Solution

Yi Zhao; Valentin Gies; Jean-Marc Ginoux

doi:10.21307/ijssis-2017-787

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WSN Based Thermal Modeling: A New Indoor Energy Efficient Solution

International Journal on Smart Sensing and Intelligent Systems

Volume 8 (2015): Issue 2 (January 2015)

By: Yi Zhao, Valentin Gies and Jean-Marc Ginoux

Open Access

|Jun 2015

Abstract

In this paper, we proposed to use the Efficient Indoor Thermal Time Constant (EITTC) to characterize the indoor thermal response in old buildings. Accordingly, a low cost, energy-efficient, wide-applicable indoor thermal modeling solution is developed by combining Wireless Sensor Network (WSN) and Artificial Neural Network (ANN). Experiments on both prototype and building room showed consistent results that the combination of WSN and ANN can provide accurate indoor thermal models. A linear approximation of these models makes it possible to estimate the EITTC of building room. Statistical computations confirmed these estimations by showing a strong correlation between the model’s predicted EITTC and measured data. Thus the indoor thermal response under different indoor/outdoor conditions can be characterized. Finally, a model based adaptive heating Start/Shut control method is proposed and tested, with which, direct energy saving is achieved.

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Articles in this issue

DOI: https://doi.org/10.21307/ijssis-2017-787 | Journal eISSN: 1178-5608

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Language: English

Page range: 869 - 895

Submitted on: Jan 15, 2015

Accepted on: Mar 27, 2015

Published on: Jun 1, 2015

Published by: Macquarie University, Australia

In partnership with: Paradigm Publishing Services

Publication frequency: 1 issue per year

Keywords:

Wireless Sensors Network,

Back-propagation Neural Network,

Thermal Modeling,

Linear Approximations,

Effective Indoor Thermal Time Constant

Related subjects:

Engineering,

Introductions and overviews,

Engineering, other

© 2015 Yi Zhao, Valentin Gies, Jean-Marc Ginoux, published by Macquarie University, Australia
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Volume 8 (2015): Issue 2 (January 2015)